基于Nelson-Siegel模型的利率期限结构研究

以上海证券交易所固定利率附息国债为研究对象,选取2006年7月～2011年6月每月最后一个交易日的数据,以观察利率期限结构的变化情况。研究得出,随着期限的增加,国债利率期限结构呈现上升趋势,这与流动性偏好理论相一致;不同期限的即期利率的相关性很强,基本表现为\"同涨同跌\"的特点。     

Do the Hamilton and Beveridge–Nelson filters provide the same information about output gaps? An empirical comparison for practitioners

I compare the empirical performances of the recently-developed Hamilton and Beveridge–Nelson filters of nonstationary time series, using quarterly data on real gross state product in U.S. states. There is meaningful overlap between the two filters, with average correlation coefficients ranging between 0.60 and 0.97. The Hamilton filter and its more recent modification produce cycles of greater volatility and amplitude than the Beveridge–Nelson filter and appear to outperform in pseudo-out-of-sample forecasting exercises of future GSP growth and inflation (though the outperformance is not generally statistically significant). The Beveridge–Nelson filter is, however, less sensitive to realizations of new data.     

Labor productivity forecasts based on a Beveridge–Nelson filter: Is there statistical evidence for a slowdown?

In this paper, I assess the evidence for a structural break in labor productivity growth in the years before the Great Recession with the use of out-of-sample forecasting exercises for the years 2010 to 2019 and the recently developed Beveridge–Nelson filter. Models based on a Beveridge–Nelson filter with no structural breaks outperform those allowing for a structural break, and there is statistically significant evidence that they outperform the random walk, though all models were too optimistic about labor productivity growth. Recently developed statistical tests do point to the presence of a structural break before the Great Recession, but uncertainty about the data-generating process for labor productivity growth or the timing and magnitude of the break may be too great to be helpful in forecast preparation.     

Testing the significance and the non-linearity of the Phillips trade-off in the Euro Area

This paper tests the significance and the non-linearity of the Phillips trade-off in the aggregate Euro Area, in an unobserved components model of stochastic NAIRU and trend output featuring the Phillips equation and the Okun law as main identifying equations, with quarterly data for 1970:I-2002:III. The Phillips curve turns out to be linear and its trade-off statistically significant, while non-linearity shows up in the Okun relation. The results prove to be robust to alternative lag length structures of the model, and to alternative non-linear functional forms. The trend-cycle decompositions estimated with the model capture the main features of the Euro Area’s recent macroeconomic record.First version received: 1 September 2003 / Final version received: June 2004CEMPRE - Centro de Estudos Macroeconómicos e Previsão - is supported by the Fundação para a Ciência e a Tecnologia, Portugal, through the Programa Operacional Ciência, Tecnologia e Inovação (POCTI) of the Quadro Comunitário de Apoio III, which is financed by FEDER and Portuguese funds.We thank comments on earlier versions by Fabio Canova, Miguel St Aubyn, Alvaro Almeida, Pete Richardson, Kevin Ross, and two anonymous referees. We acknowledge James D. Hamilton’s help with the confidence bands. The usual disclaimer applies.     

Testing for the stability of money demand in Italy: has the Euro influenced the monetary transmission mechanism?

Stability of money demand is a crucial issue for the efficacy of monetary policy. This is particularly true in the presence of significant exogenous shocks to the monetary system. By implementing the most recent econometric testing procedures, this article intends to investigate the consistency of the stability of money demand in Italy, one of the larger European Monetary Union (EMU) countries, before and after the EMU. Among others, the objective is, indeed, to ascertain the effect of a change in the currency regime on the monetary aggregates and to provide a valid empirical model which is a viable tool for policy performance.     

Modelling trigonometric seasonal components for monthly economic time series

The basic structural time series model has been designed for the modelling and forecasting of seasonal economic time series. In this article, we explore a generalization of the basic structural time series model in which the time-varying trigonometric terms associated with different seasonal frequencies have different variances for their disturbances. The contribution of the article is two-fold. The first aim is to investigate the dynamic properties of this frequency-specific Basic Structural Model (BSM). The second aim is to relate the model to a comparable generalized version of the Airline model developed at the US Census Bureau. By adopting a quadratic distance metric based on the restricted reduced form moving-average representation of the models, we conclude that the generalized models have properties that are close to each other compared to their default counterparts. In some settings, the distance between the models is almost zero so that the models can be regarded as observationally equivalent. An extensive empirical study on disaggregated monthly shipment and foreign trade series illustrates the improvements of the frequency-specific extension and investigates the relations between the two classes of models.     

System estimates of potential output and the NAIRU

This paper proposes a new approach for estimating potential output and the NAIRU. The methodology models these key unobservable economic variables as latent stochastic trends within a trivariate system of observables comprising information on unemployment, GDP, and inflation. Identification is achieved through the use of a standard version of Okun's law and a Phillips curve. The performance of the procedure is investigated using Swedish quarterly data covering the time period 1970:1–1996:3. First version received: June 1997/final version received: September 1998     

Nowcasting sales growth of manufacturing companies in India

The performance of private corporate sector is used as an important demand indicator for monetary policy making. As these data are received with a lag, assessing and monitoring of corporate sales on a real-time basis poses a significant challenge to policy makers in India. In this context, this article attempts to nowcast quarterly sales growth of Indian manufacturing companies and GDP growth of India using dynamic factor modelling framework. A multiple-level framework through turning point analysis and elastic net structure is used to overcome the overfitting problem during variable selection. Empirical results show improvement in forecast accuracy for one quarter ahead nowcast using 3-factor and 4-factor models over the benchmark model. However, absolute dominance of 3-factor models over 4-factor models was not established. As such, the article has proposed a forecast combination technique to nowcast sales growth of manufacturing companies in India.     

State space models for the exchange rate pass-through: determinants and null/full pass-through hypotheses

In this article, we formulate linear Gaussian state space models for the estimation of the exchange rate pass-through of the Brazilian Real against the US Dollar, using monthly data from August 1999 to August 2008. The state space/Kalman filtering framework allows the investigation of some empirical aspects previously suggested in the literature, such as time-varying coefficients and null/full pass-through hypotheses. We also test whether some theoretical ‘determinants’ of the pass-through are statistically significant in the period considered. The principal findings are as follows: (1) the data offer strong support to a time-varying pass-through; and (2) the variance of the exchange rate pass-through, the monetary policy and the trade flow have shown to be relevant determinants of the exchange rate pass-through.